Mobile computing devices are quickly becoming ubiquitous tools for the average consumer. Mobile computing devices, such as smart phones, smart glasses, tablet computers, and the like, may be used for a variety of purposes including work, entertainment, and information research. As mobile computing devices become more ingrained into the everyday life of users, alternative or additional modes of interacting with the mobile computing devices are becoming ever more important. For example, hands-free operation (e.g., via voice commands) and gesture control (e.g., via hand gestures) are popular alternative mechanisms to control mobile computing devices.
Gesture control of mobile computing devices often involves the analysis of images captured by a camera, which may be integrated with or communicatively coupled to the mobile computing device. The captured images are analyzed to detect hand gestures performed by a user, which are typically interpreted as commands for the mobile computing device. Various kinds of image analysis techniques may be used to detect a user input gesture. For example, hand feature detection and/or skin detection algorithms may be used to identify whether a hand is actually present in the captured image. However, some image analysis algorithms or techniques may perform better under certain conditions, while others may perform poorly. Additionally, computing resources are often limited on mobile computing devices, which may limit the robustness and performance of the image analysis.